Using in vivo functional and structural connectivity to predict chronic stroke aphasia deficits

Abstract Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain–behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.


Acquisition of neuroimaging data and pre-processing
All patients had high resolution structural T1-weighted MRI scans, which were acquired on a 3.0 Tesla Philips Achieva scanner (Philips Healthcare, Best, The Netherlands) using an 8element SENSE head coil. A T1-weighted inversion recovery sequence with 3D acquisition was employed, with the following parameters: TR (repetition time) = 9.0 ms, TE (echo time) We acquired rs-fMRI on a subset of patients (N = 39) and younger healthy controls N = 30).
We used a dual gradient echo planar imaging technique for the rs-fMRI protocol in order to improve signal detection within inferior temporal and orbitofrontal regions [10][11][12] . We used the following parameters: TEs = 12 and 35 ms, TR = 2.8 s, voxel size = 3 × 3 × 4 mm, FOV = 240 × 124 × 240 mm, matrix size = 80 × 80, flip angle = 85°. A total of 130 volumes were collected over 6.25 min. Participants were instructed to look at a fixation cross and lie still during scanning.
A peripheral pulse unit was placed on the participant's index finger to measure the cardiac cycle and aimed at reducing artefacts associated with pulsatile brain movements 13 . The total scan time was approximately 28 minutes. Each diffusion-weighted volume was acquired entirely before starting on the next diffusion weighting, resulting in 44 temporally spaced volumes with different gradient directions. For each run, phase encoding was performed in right-left and left-right directions, giving two sets of images with the same diffusion gradient directions but opposite polarity k-space traversal, and hence reversed phase and frequency encoded direction 14 .
High-resolution structural scans were pre-processed with the same procedure as our previous studies 15, 16 using Statistical Parametric Mapping software (SPM8: Wellcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/) and a modified segmentation-normalisation procedure 17 . After normalising individual lesioned brain images into standard Montreal Neurological Institute (MNI) space, images were smoothed with an 8mm full-width-halfmaximum (FWHM) Gaussian kernel. Lesions were automatically identified for each patient by comparing the structural image with an age and education matched control group, using an outlier detection algorithm to identify 'abnormal' voxels 17 . All parameters were kept at default except the lesion definition 'U-threshold', which was set 0.5 after comparing the results obtained from a sample of patients to what would be nominated as lesioned tissue by an expert neurologist. All resultant lesion maps were visually inspected and any discrepancies were manually corrected. The overall lesion maps were concatenated across the patients in the current study and shown in Figure 1.
The resting-state data were processed using SPM8 and the Data Processing Assistant for Resting State fMRI (DPARSF Advanced Edition, version 2.3) toolbox 18 . The pre-processing pipeline for the functional data included: discarding the first two time-points for signal stabilisation, slice time correction, volume realignment, dual-echo combination (linear average), and co-registration to the T1. All functional data were then inspected using an artefact rejection tool (ART; http://www.nitrc.org/projects/artifact_detect/) in order to identify timepoints with high motion and/or signal artefacts. Volumes were censored as outliers if they had an intensity ± 2.5 standard deviations from the mean intensity and/or had greater than 1 mm movement in any direction. In addition to regressing out outlier volumes, we added 24 movement parameters as regressors in order to account for movement related artefacts 19 . We used DPARSFA to regress out the influence of nuisance variables, including global signal, white matter signal, mean CSF signal, and the 24 movement parameters so that the effects of head movements could be better controlled 20,21 . The images were normalized using the nativeto-MNI transformation matrix obtained from the modified normalisation procedure by Seghier and colleagues 17 and then smoothed using 8 mm FWHM Gaussian kernel. Finally, we removed a linear trend and filtered the time series between 0.01 ~ 0.08 Hz.
Processing of the DTI data was conducted using FSL's (v5.0.10) diffusion pipeline 22 . Firstly, a brain extraction was performed on the B0 image using the brain extraction tool (BET) 23 . The data were prepared and submitted to FSL's TOPUP tool in order to estimate and correct susceptibility inducted distortions 24 , where the off-resonance field is estimated and then the two images are combined into a single corrected one. The eddy tool was used to correct for distortions such as eddy currents and head motion 25

T1 predicting behaviours in regularised regression models
For completeness, we also re-analysed the T1 data using the same multivariate modelling

Figure.
Mapping lesion to behavioural factor scores using multivariate regularise regression models (optimising alpha using nested cross validation). 5a shows the distribution of optimal alpha (i.e., 0 is dense and 1 is sparse) from each training fold. 5b shows the neural projections of the beta weights from the regularised model averaged across all training folds.

Alpha values distribution when optimised in training sets
Since alpha was trained in the training set, each fold of training set had its own optimal alpha value. In the final model, the optimal alpha is not a single best value but a distribution of alpha values produced from each training set. The distribution is not even. However, there is a most

Functional connectivity of lesion predictors
Lesion correlates for four behavioural factor scores in the stroke aphasic population (Supplementary Materials 7 Figure). The identified clusters were used as seeds in the healthy rs-fMRI dataset in order to identify typical functional connectivity networks associated with these critical regions per behaviour (i.e., phonology, semantics, speech fluency and executive function).

Figure.
The significant regions using the whole patient group (68 patients). The threshold was voxel FDR corrected p < 0.05 and cluster > 2 cm 3 . The ROIs in phonology, semantics, and fluency were used as seed ROIs in the FC analysis of the healthy control group.